I am going to calculate models fairness. I use adult income dataset. Models that I use are Random Forest Regressor and Logistic Regression.
First model that I use is RandomForestClassifier. I have already used RandomForestRegressor before. Difference is that Classifier outputs probability distribution over classes, not a single number. Later I apply bias mitigation methode 'roc-pivot' to the explainer.
recall precision f1 accuracy auc RandomForestClassifier 0.253273 0.896698 0.394983 0.81434 0.883648
recall precision f1 accuracy auc
RandomForestClassifier 0.177719 0.982033 0.300971 0.802465 0.883782
Found NaN's or 0's for models: {'RandomForestClassifier'}
It is advisable to check 'metric_ratios'
Logistic regression aims to solve classification problems. It does this by predicting categorical outcomes, unlike linear regression that predicts a continuous outcome.
recall precision f1 accuracy auc LogisticRegression 0.330966 0.599597 0.426508 0.787028 0.809132